All the conclusions, results, and recommendations are derived according to this dataset which is not considering the World Wars as terrorist acts and did not waste the lives of millions of people.
I have selected the Global Terrorism dataset to investigate it and do some Exploratory Data Analysis by exploring the correlations between the varies things on it and find patterns to find out the hot zones of terrorism and get all security issues and insights I can derive by answering some question that we will ask now.
This section is to import the necessary liberaries and DataFrame
# for data frames
import pandas as pd
# for numerical fn.
import numpy as np
# for data visualization
import matplotlib.pyplot as plt
%matplotlib inline
import datetime as dt
from pandas_profiling import ProfileReport
C:\Users\ip\AppData\Local\Temp\ipykernel_9692\1908016125.py:9: DeprecationWarning: `import pandas_profiling` is going to be deprecated by April 1st. Please use `import ydata_profiling` instead. from pandas_profiling import ProfileReport
# pip install ipywidgets
# pip install pandas-profiling
In this section we will do some gathering, assessing and cleaning to the data to be more suatable and easy to analyse
df = pd.read_csv("globalterrorismdb_0718dist.csv", encoding='latin-1')
C:\Users\ip\AppData\Local\Temp\ipykernel_9692\2266119131.py:1: DtypeWarning: Columns (4,6,31,33,61,62,63,76,79,90,92,94,96,114,115,121) have mixed types. Specify dtype option on import or set low_memory=False.
df = pd.read_csv("globalterrorismdb_0718dist.csv", encoding='latin-1')
df.shape
(181691, 135)
Now we know that the data has about 180k observations and 135 column.
Lets look at 3 random observations.
# to see all columns
pd.set_option('display.max_columns', None)
df.sample(3)
| eventid | iyear | imonth | iday | approxdate | extended | resolution | country | country_txt | region | region_txt | provstate | city | latitude | longitude | specificity | vicinity | location | summary | crit1 | crit2 | crit3 | doubtterr | alternative | alternative_txt | multiple | success | suicide | attacktype1 | attacktype1_txt | attacktype2 | attacktype2_txt | attacktype3 | attacktype3_txt | targtype1 | targtype1_txt | targsubtype1 | targsubtype1_txt | corp1 | target1 | natlty1 | natlty1_txt | targtype2 | targtype2_txt | targsubtype2 | targsubtype2_txt | corp2 | target2 | natlty2 | natlty2_txt | targtype3 | targtype3_txt | targsubtype3 | targsubtype3_txt | corp3 | target3 | natlty3 | natlty3_txt | gname | gsubname | gname2 | gsubname2 | gname3 | gsubname3 | motive | guncertain1 | guncertain2 | guncertain3 | individual | nperps | nperpcap | claimed | claimmode | claimmode_txt | claim2 | claimmode2 | claimmode2_txt | claim3 | claimmode3 | claimmode3_txt | compclaim | weaptype1 | weaptype1_txt | weapsubtype1 | weapsubtype1_txt | weaptype2 | weaptype2_txt | weapsubtype2 | weapsubtype2_txt | weaptype3 | weaptype3_txt | weapsubtype3 | weapsubtype3_txt | weaptype4 | weaptype4_txt | weapsubtype4 | weapsubtype4_txt | weapdetail | nkill | nkillus | nkillter | nwound | nwoundus | nwoundte | property | propextent | propextent_txt | propvalue | propcomment | ishostkid | nhostkid | nhostkidus | nhours | ndays | divert | kidhijcountry | ransom | ransomamt | ransomamtus | ransompaid | ransompaidus | ransomnote | hostkidoutcome | hostkidoutcome_txt | nreleased | addnotes | scite1 | scite2 | scite3 | dbsource | INT_LOG | INT_IDEO | INT_MISC | INT_ANY | related | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 135329 | 201407210019 | 2014 | 7 | 21 | NaN | 0 | NaN | 4 | Afghanistan | 6 | South Asia | Faryab | Qaysar district | 35.687416 | 64.293102 | 3.0 | 0 | NaN | 07/21/2014: A roadside bomb detonated in Qaysa... | 1 | 1 | 1 | 0.0 | NaN | NaN | 0.0 | 1 | 0 | 3 | Bombing/Explosion | NaN | NaN | NaN | NaN | 14 | Private Citizens & Property | 67.0 | Unnamed Civilian/Unspecified | Not Applicable | Civilians | 4.0 | Afghanistan | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Unknown | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | 0 | -99.0 | 0.0 | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 6 | Explosives | 16.0 | Unknown Explosive Type | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | A roadside bomb was used in the attack. | 2.0 | 0.0 | 0.0 | 4.0 | 0.0 | 0.0 | 0 | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | The victims were family members of an Afghan N... | "Afghanistan: Two Civilians, 15 Insurgents, 8 ... | NaN | NaN | START Primary Collection | -9 | -9 | 0 | -9 | NaN |
| 6072 | 197803060001 | 1978 | 3 | 6 | NaN | 0 | NaN | 130 | Mexico | 1 | North America | Jalisco | Guadalajara | 20.673343 | -103.344177 | 1.0 | 0 | NaN | NaN | 1 | 1 | 1 | 0.0 | NaN | NaN | 0.0 | 1 | 0 | 3 | Bombing/Explosion | NaN | NaN | NaN | NaN | 1 | Business | 3.0 | Bank/Commerce | NaN | Bank | 130.0 | Mexico | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Union of the People (UDP) | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | 0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 6 | Explosives | 16.0 | Unknown Explosive Type | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Explosive | NaN | NaN | NaN | NaN | NaN | NaN | 1 | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | PGIS | 0 | 0 | 0 | 0 | NaN |
| 171289 | 201701180030 | 2017 | 1 | 18 | January 15-21, 2017 | 0 | NaN | 45 | Colombia | 3 | South America | Unknown | Unknown | NaN | NaN | 5.0 | 0 | The incident occurred in either Norte de Santa... | 01/00/2017: Sometime between January 15, 2017 ... | 1 | 1 | 1 | 0.0 | NaN | NaN | 1.0 | 1 | 0 | 3 | Bombing/Explosion | NaN | NaN | NaN | NaN | 21 | Utilities | 108.0 | Oil | Ecopetrol | Cano Limon-Covenas Oil Pipeline | 45.0 | Colombia | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | National Liberation Army of Colombia (ELN) | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | 0 | -99.0 | 0.0 | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 6 | Explosives | 16.0 | Unknown Explosive Type | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1 | 4.0 | Unknown | -99.0 | An oil pipeline was damaged in this attack. | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | "Bombings halted pumping at Colombia's Cano-Li... | "Wave of Attacks Hit Colombia Pipeline," Energ... | NaN | START Primary Collection | 0 | 0 | 0 | 0 | 201701180024, 201701180025, 201701180026, 2017... |
After gathering the data it has some problems and needs to be cleand so let's take a look on some of these problems.
Firstly there's many columns that we will not use lets take what we need from columns.
df = df[["eventid","iday", "iyear", "imonth", "country_txt", "region_txt", "provstate", "city", "latitude", "longitude", "success", "attacktype1_txt","nkill", "target1"]]
df.shape
(181691, 14)
Now it has 15 columns
df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 181691 entries, 0 to 181690 Data columns (total 14 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 eventid 181691 non-null int64 1 iday 181691 non-null int64 2 iyear 181691 non-null int64 3 imonth 181691 non-null int64 4 country_txt 181691 non-null object 5 region_txt 181691 non-null object 6 provstate 181270 non-null object 7 city 181257 non-null object 8 latitude 177135 non-null float64 9 longitude 177134 non-null float64 10 success 181691 non-null int64 11 attacktype1_txt 181691 non-null object 12 nkill 171378 non-null float64 13 target1 181055 non-null object dtypes: float64(3), int64(5), object(6) memory usage: 19.4+ MB
df[df.city == "Unknown"].sample(2)
| eventid | iday | iyear | imonth | country_txt | region_txt | provstate | city | latitude | longitude | success | attacktype1_txt | nkill | target1 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 54327 | 199212030028 | 3 | 1992 | 12 | France | Western Europe | Corsica | Unknown | 41.918891 | 8.737554 | 1 | Bombing/Explosion | 0.0 | Office |
| 168072 | 201610130036 | 13 | 2016 | 10 | India | South Asia | Uttar Pradesh | Unknown | 26.846708 | 80.946159 | 0 | Assassination | 0.0 | Vehicle of Consultant of Chief Minister: Sanja... |
I've noticed that there's many Unknown cities
lets make it null values
df.replace("Unknown", np.nan, inplace=True)
df[df.iday == 0].sample(2)
| eventid | iday | iyear | imonth | country_txt | region_txt | provstate | city | latitude | longitude | success | attacktype1_txt | nkill | target1 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 5334 | 197710000002 | 0 | 1977 | 10 | Colombia | South America | Antioquia | Medellin | 6.242026 | -75.564766 | 1 | Hostage Taking (Kidnapping) | 0.0 | Gilberto Restrepo Velez, landowner |
| 102946 | 201109010010 | 0 | 2011 | 9 | India | South Asia | Chhattisgarh | Raipur | 21.251384 | 81.629641 | 0 | Bombing/Explosion | 0.0 | The targets of the attempted bombing were poli... |
There's many days and months is 0
let's make it Null
df[["iday", "imonth"]] = df[["iday", "imonth"]].replace(0, np.nan)
df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 181691 entries, 0 to 181690 Data columns (total 14 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 eventid 181691 non-null int64 1 iday 180800 non-null float64 2 iyear 181691 non-null int64 3 imonth 181671 non-null float64 4 country_txt 181691 non-null object 5 region_txt 181691 non-null object 6 provstate 176980 non-null object 7 city 171482 non-null object 8 latitude 177135 non-null float64 9 longitude 177134 non-null float64 10 success 181691 non-null int64 11 attacktype1_txt 174415 non-null object 12 nkill 171378 non-null float64 13 target1 175137 non-null object dtypes: float64(5), int64(3), object(6) memory usage: 19.4+ MB
df.dropna(inplace=True)
df.duplicated().sum()
0
df.drop_duplicates(inplace=True)
df.sample(2)
| eventid | iday | iyear | imonth | country_txt | region_txt | provstate | city | latitude | longitude | success | attacktype1_txt | nkill | target1 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 44845 | 199012110011 | 11.0 | 1990 | 12.0 | Philippines | Southeast Asia | Metropolitian Manila | Manila | 14.596051 | 120.978666 | 1 | Assassination | 1.0 | Patrolman assigned to western police dist. hom... |
| 73362 | 200111150006 | 15.0 | 2001 | 11.0 | Sri Lanka | South Asia | Eastern | Batticaloa | 7.733107 | 81.688820 | 1 | Bombing/Explosion | 4.0 | Former Liberation Tigers of Tamil Eelam (LTTE)... |
Now lets creat a new columns calles date
df["date"] = pd.to_datetime(dict(year = df.iyear, month = df.imonth, day = df.iday))
Also lets make the 'eventid' as index for our data then drop it.
df.set_index(df.eventid, inplace=True)
df.sample()
| eventid | iday | iyear | imonth | country_txt | region_txt | provstate | city | latitude | longitude | success | attacktype1_txt | nkill | target1 | date | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| eventid | |||||||||||||||
| 200703270013 | 200703270013 | 27.0 | 2007 | 3.0 | Iraq | Middle East & North Africa | Baghdad | Baghdad | 33.303566 | 44.371773 | 1 | Armed Assault | 3.0 | Waqf Officers in Baghdad | 2007-03-27 |
df.eventid.duplicated().sum()
0
df.drop(columns="eventid",inplace=True)
As a final step in cleaning data lets rename all columns to be easier to read.
df.rename(columns={"iday" : "day", "iyear" : "year", "imonth" : "month", "region_txt" : "region", "provstate" : "state", "attacktype1_txt" : "attack_type", "target1" : "target", "country_txt" : "country"}, inplace=True)
df.head(3)
| day | year | month | country | region | state | city | latitude | longitude | success | attack_type | nkill | target | date | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| eventid | ||||||||||||||
| 197001010002 | 1.0 | 1970 | 1.0 | United States | North America | Illinois | Cairo | 37.005105 | -89.176269 | 1 | Armed Assault | 0.0 | Cairo Police Headquarters | 1970-01-01 |
| 197001020001 | 2.0 | 1970 | 1.0 | Uruguay | South America | Montevideo | Montevideo | -34.891151 | -56.187214 | 0 | Assassination | 0.0 | Juan Maria de Lucah/Chief of Directorate of in... | 1970-01-02 |
| 197001020002 | 2.0 | 1970 | 1.0 | United States | North America | California | Oakland | 37.791927 | -122.225906 | 1 | Bombing/Explosion | 0.0 | Edes Substation | 1970-01-02 |
df.day = df.day.astype(int)
df.month = df.month.astype(int)
df.nkill = df.nkill.astype(int)
df.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 147519 entries, 197001010002 to 201712310031 Data columns (total 14 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 day 147519 non-null int32 1 year 147519 non-null int64 2 month 147519 non-null int32 3 country 147519 non-null object 4 region 147519 non-null object 5 state 147519 non-null object 6 city 147519 non-null object 7 latitude 147519 non-null float64 8 longitude 147519 non-null float64 9 success 147519 non-null int64 10 attack_type 147519 non-null object 11 nkill 147519 non-null int32 12 target 147519 non-null object 13 date 147519 non-null datetime64[ns] dtypes: datetime64[ns](1), float64(2), int32(3), int64(2), object(6) memory usage: 15.2+ MB
df.describe()
| day | year | month | latitude | longitude | success | nkill | |
|---|---|---|---|---|---|---|---|
| count | 147519.000000 | 147519.000000 | 147519.000000 | 147519.000000 | 1.475190e+05 | 147519.000000 | 147519.000000 |
| mean | 15.587802 | 2003.027454 | 6.471336 | 23.884283 | -5.552770e+02 | 0.904541 | 2.307974 |
| std | 8.767093 | 12.979404 | 3.389837 | 18.694646 | 2.243946e+05 | 0.293849 | 11.703643 |
| min | 1.000000 | 1970.000000 | 1.000000 | -53.154613 | -8.618590e+07 | 0.000000 | 0.000000 |
| 25% | 8.000000 | 1991.000000 | 4.000000 | 11.707204 | 5.908448e+00 | 1.000000 | 0.000000 |
| 50% | 15.000000 | 2009.000000 | 6.000000 | 31.633078 | 4.349141e+01 | 1.000000 | 0.000000 |
| 75% | 23.000000 | 2014.000000 | 9.000000 | 34.816667 | 6.902103e+01 | 1.000000 | 2.000000 |
| max | 31.000000 | 2017.000000 | 12.000000 | 74.633553 | 1.793667e+02 | 1.000000 | 1570.000000 |
Now we have cleaned data without missing or duplicated values and ready to be explored
After wrangling the data, In this section we will answer some questions by analysing the data to create some Conclusions about the dataframe. All these questions is from my deep mind and of course you may have different questions so don't be restricted by this questions.
ProfileReport(df)
Summarize dataset: 0%| | 0/5 [00:00<?, ?it/s]
Generate report structure: 0%| | 0/1 [00:00<?, ?it/s]
Render HTML: 0%| | 0/1 [00:00<?, ?it/s]
df.sample(2)
| day | year | month | country | region | state | city | latitude | longitude | success | attack_type | nkill | target | date | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| eventid | ||||||||||||||
| 201403090024 | 9 | 2014 | 3 | Iraq | Middle East & North Africa | Saladin | Hay al-Qadisiyah | 34.458889 | 43.791004 | 1 | Assassination | 1 | Mayor | 2014-03-09 |
| 201407050047 | 5 | 2014 | 7 | Afghanistan | South Asia | Zabul | Shahjoy district | 32.503195 | 67.415893 | 1 | Bombing/Explosion | 2 | Vehicle | 2014-07-05 |
def pltbar(datax, datay, namex, namey, title):
plt.bar(
x = datax,
height = datay
)
plt.xlabel(namex, fontsize = 15)
plt.ylabel(namey, fontsize = 15)
plt.title(title, fontsize = 15)
# City
print("Top 5 countries\n",df.country.value_counts()[:5])
Top 5 countries Iraq 21066 Pakistan 12987 India 10163 Afghanistan 9523 Colombia 6379 Name: country, dtype: int64
pltbar(df.country.value_counts()[:5].index, df.country.value_counts()[:5].values, "Country", "# Terrorism act", "Top 5 Countries")
# State
print("Top 5 Governates\n",df.state.value_counts()[:5])
pltbar(df.state.value_counts()[:5].index, df.state.value_counts()[:5].values, "Governate", "# Terrorism act", "Top 5 Governates")
plt.xticks(rotation = 45);
Top 5 Governates Baghdad 7129 Northern Ireland 4158 Balochistan 3255 Sindh 3030 Saladin 2838 Name: state, dtype: int64
# Region
print("Top 5 Regions\n",df["region"].value_counts()[:5])
pltbar(df.region.value_counts()[:5].index, df.region.value_counts()[:5].values, "Region", "# Terrorism act", "Top 5 Regions")
plt.xticks(rotation = 45);
Top 5 Regions Middle East & North Africa 41296 South Asia 37717 South America 15001 Western Europe 14498 Sub-Saharan Africa 13534 Name: region, dtype: int64
# type
print("Top 5 Type of attacks\n",df.attack_type.value_counts()[:5])
pltbar(df.attack_type.value_counts()[:5].index, df.attack_type.value_counts()[:5].values, "Type", "# Terrorism act", "Top 5 Type of attacks")
plt.xticks(rotation = 45);
Top 5 Type of attacks Bombing/Explosion 74250 Armed Assault 36442 Assassination 17897 Facility/Infrastructure Attack 9150 Hostage Taking (Kidnapping) 7529 Name: attack_type, dtype: int64
# target
print("Top 5 Targets\n",df.target.value_counts()[:5])
pltbar(df.target.value_counts()[:5].index, df.target.value_counts()[:5].values, "Target", "# Terrorism act", "Top 5 Targets")
Top 5 Targets Civilians 5688 Patrol 2693 Vehicle 2533 Checkpoint 2411 Soldiers 2261 Name: target, dtype: int64
# success
plt.pie(df.groupby(df.success).count().day, labels=["Failed", "Succeeded"], autopct='%.2f%%',
wedgeprops={'linewidth': 2.0, 'edgecolor': 'white'},
textprops={'size': 'x-large'});
plt.title('Attack Success', fontsize=18)
df.groupby(df.success).count().day
success 0 14082 1 133437 Name: day, dtype: int64
# Year
df.year.value_counts()[:5]
2014 13721 2015 11938 2016 10540 2013 10407 2017 8341 Name: year, dtype: int64
# Numerical Data
df.describe()
| day | year | month | latitude | longitude | success | nkill | |
|---|---|---|---|---|---|---|---|
| count | 147519.000000 | 147519.000000 | 147519.000000 | 147519.000000 | 1.475190e+05 | 147519.000000 | 147519.000000 |
| mean | 15.587802 | 2003.027454 | 6.471336 | 23.884283 | -5.552770e+02 | 0.904541 | 2.307974 |
| std | 8.767093 | 12.979404 | 3.389837 | 18.694646 | 2.243946e+05 | 0.293849 | 11.703643 |
| min | 1.000000 | 1970.000000 | 1.000000 | -53.154613 | -8.618590e+07 | 0.000000 | 0.000000 |
| 25% | 8.000000 | 1991.000000 | 4.000000 | 11.707204 | 5.908448e+00 | 1.000000 | 0.000000 |
| 50% | 15.000000 | 2009.000000 | 6.000000 | 31.633078 | 4.349141e+01 | 1.000000 | 0.000000 |
| 75% | 23.000000 | 2014.000000 | 9.000000 | 34.816667 | 6.902103e+01 | 1.000000 | 2.000000 |
| max | 31.000000 | 2017.000000 | 12.000000 | 74.633553 | 1.793667e+02 | 1.000000 | 1570.000000 |
pltbar(df.groupby(df.country).sum().sort_values(by="nkill", ascending=False)[:5].nkill.index, df.groupby(df.country).sum().sort_values(by="nkill", ascending=False)[:5].nkill.values, "Country", "#of kills", "Top 5 countries has kills")
df.groupby(df.country).sum().sort_values(by="nkill", ascending=False)[:5].nkill
country Iraq 68408 Afghanistan 31385 Pakistan 22552 Nigeria 21201 India 16236 Name: nkill, dtype: int32
Q2: What is the top 5 countries in average kills by attack?
pltbar(df.groupby(df.country).mean().sort_values("nkill", ascending=False)[1:6].index, df.groupby(df.country).mean().sort_values("nkill", ascending=False).nkill[1:6].values, "Country", "Average kills by attack", "Top 5 countries in average kills by attack")
df.groupby(df.country).mean().sort_values("nkill", ascending=False)[:6]
| day | year | month | latitude | longitude | success | nkill | |
|---|---|---|---|---|---|---|---|
| country | |||||||
| South Vietnam | 15.000000 | 1972.000000 | 6.000000 | 13.977956 | 108.002298 | 1.000000 | 81.000000 |
| Barbados | 14.000000 | 1981.333333 | 6.333333 | 13.126551 | -59.545478 | 1.000000 | 25.333333 |
| Rwanda | 14.322835 | 1998.464567 | 5.031496 | -1.958385 | 29.835836 | 0.968504 | 19.811024 |
| South Sudan | 15.172662 | 2015.402878 | 6.323741 | 6.564728 | 30.774401 | 0.906475 | 14.323741 |
| Djibouti | 15.882353 | 1992.764706 | 6.117647 | 11.584890 | 42.953165 | 1.000000 | 11.647059 |
| Niger | 14.917431 | 2008.788991 | 5.972477 | 15.051038 | 8.122337 | 0.963303 | 10.532110 |
Q3: What is the most target in each country?
for x in df.groupby(df.country).sum().sort_values(by="country").index:
print("The most target in",x,"is",df[df.country == x].groupby("target").count().sort_values('nkill',ascending = False)[:1].index[0],"with",df[df.country == x].groupby("target").count().sort_values('nkill',ascending = False)[:1].day.values,"attacks")
The most target in Afghanistan is Vehicle with [659] attacks The most target in Albania is bridge with [3] attacks The most target in Algeria is Unit with [55] attacks The most target in Angola is High tension line tower with [113] attacks The most target in Argentina is office with [19] attacks The most target in Armenia is Andranik Markarian with [1] attacks The most target in Australia is Church with [8] attacks The most target in Austria is Embassy Official with [5] attacks The most target in Azerbaijan is Afiyaddin Jalilou, Deputy to Parliament Chairman with [1] attacks The most target in Bahamas is Car and home of Norman Solomon leader of Social Democratic Party with [1] attacks The most target in Bahrain is Officers with [28] attacks The most target in Bangladesh is Bus with [60] attacks The most target in Barbados is British West Indies Airways Offices with [1] attacks The most target in Belarus is A prison was targeted in the attack. with [1] attacks The most target in Belgium is Embassy Official with [3] attacks The most target in Belize is Car of Minister of State, Mr. V.H. Courtenay with [1] attacks The most target in Benin is Bar-Restaurant with [1] attacks The most target in Bhutan is Bhutan Oil Distributor's petrol pump with [1] attacks The most target in Bolivia is office with [12] attacks The most target in Bosnia-Herzegovina is Unk with [5] attacks The most target in Botswana is Abraham Onkgopotse Tiro, organizer with [1] attacks The most target in Brazil is Multinational with [9] attacks The most target in Brunei is Embassy Official with [5] attacks The most target in Bulgaria is Mosque with [2] attacks The most target in Burkina Faso is Police Station with [5] attacks The most target in Burundi is Bar with [24] attacks The most target in Cambodia is unit with [5] attacks The most target in Cameroon is Civilians with [33] attacks The most target in Canada is Mosque with [4] attacks The most target in Central African Republic is Civilians with [25] attacks The most target in Chad is Market with [6] attacks The most target in Chile is Bus with [55] attacks The most target in China is Bus with [11] attacks The most target in Colombia is Bus with [140] attacks The most target in Comoros is Prefecture building with [2] attacks The most target in Costa Rica is Nicaraguan Consulate with [2] attacks The most target in Croatia is Unit with [3] attacks The most target in Cuba is Hotel with [6] attacks The most target in Cyprus is Nicosia City Hall with [4] attacks The most target in Czech Republic is Vehicles with [2] attacks The most target in Czechoslovakia is Aircraft - Flight to Frankfurt with [1] attacks The most target in Democratic Republic of the Congo is Civilians with [82] attacks The most target in Denmark is Anti-Islam Writer: Lars Copenhagen with [1] attacks The most target in Djibouti is Cafe L'Europe with [1] attacks The most target in Dominica is Central Police Station with [1] attacks The most target in Dominican Republic is Street with [13] attacks The most target in East Germany (GDR) is Bank with [3] attacks The most target in East Timor is Alfonso Guterres with [1] attacks The most target in Ecuador is Branch with [4] attacks The most target in Egypt is Checkpoint with [217] attacks The most target in El Salvador is Military Unit with [220] attacks The most target in Equatorial Guinea is The presidential compound in Malabo, Equatorial Guinea. with [1] attacks The most target in Eritrea is A restaurant was targeted in Haykota, Gash-Barka, Eritrea. with [1] attacks The most target in Estonia is Bus?? with [1] attacks The most target in Ethiopia is Village with [10] attacks The most target in Fiji is Civilian ethnic Indian males in Labasa with [1] attacks The most target in Finland is Refugee Reception Center with [4] attacks The most target in France is Central Tax Office with [50] attacks The most target in French Guiana is Agriculture Bldg. with [1] attacks The most target in French Polynesia is Faca International Airport Terminals with [1] attacks The most target in Gabon is Activists with [1] attacks The most target in Gambia is Gambian President with [1] attacks The most target in Georgia is A peacekeeping station with [5] attacks The most target in Germany is Foreigners Hostel with [22] attacks The most target in Ghana is Vehicle, Tamale-Yendi Road with [2] attacks The most target in Greece is store with [32] attacks The most target in Grenada is Anky Elizabeth Nucete (10), Daughter Venezuelan charged affairs in Grenada with [1] attacks The most target in Guadeloupe is Airport with [1] attacks The most target in Guatemala is Military Unit with [109] attacks The most target in Guinea is President Lansana Conte with [2] attacks The most target in Guinea-Bissau is A presidential candidate with [1] attacks The most target in Guyana is Telephone Junction Box with [2] attacks The most target in Haiti is Police Station with [2] attacks The most target in Honduras is Military Unit with [9] attacks The most target in Hong Kong is 48,531 ton tanker World Bridge with [1] attacks The most target in Hungary is Car dealerships with [4] attacks The most target in Iceland is Lutheran Church with [1] attacks The most target in India is Patrol with [253] attacks The most target in Indonesia is 21 churches with [25] attacks The most target in International is Tanker ship Limberg with [1] attacks The most target in Iran is Vehicle with [8] attacks The most target in Iraq is Civilians with [2906] attacks The most target in Ireland is House with [28] attacks The most target in Israel is Civilians with [82] attacks The most target in Italy is Office with [31] attacks The most target in Ivory Coast is Base with [6] attacks The most target in Jamaica is Church with [2] attacks The most target in Japan is Shinto Shrine with [9] attacks The most target in Jordan is Offical with [4] attacks The most target in Kazakhstan is Arms Shop with [2] attacks The most target in Kenya is Vehicle with [34] attacks The most target in Kosovo is Civilians with [4] attacks The most target in Kuwait is Unit with [2] attacks The most target in Kyrgyzstan is Law enforcement offices with [3] attacks The most target in Laos is Bus with [2] attacks The most target in Latvia is A railway bypass between Zemitani and Brasla stations in the area of Riga with [1] attacks The most target in Lebanon is Patrol with [50] attacks The most target in Lesotho is American Cultural Center with [1] attacks The most target in Liberia is 4 Employees, U.N. Development Program with [1] attacks The most target in Libya is Soldiers with [65] attacks The most target in Lithuania is Border Post with [1] attacks The most target in Luxembourg is Branch with [3] attacks The most target in Macau is Vehicle with [4] attacks The most target in Macedonia is Building with [3] attacks The most target in Madagascar is 7 year old girl* with [1] attacks The most target in Malawi is Church with [1] attacks The most target in Malaysia is town with [5] attacks The most target in Maldives is Motorcycle with [3] attacks The most target in Mali is Vehicle with [41] attacks The most target in Malta is Hotel with [3] attacks The most target in Martinique is Building (owned by Chase Manhattan Bank) housing the U.S. Consulate with [1] attacks The most target in Mauritania is A director of a school with [1] attacks The most target in Mauritius is Prime Minister Sir Aeerood Jugnauth with [1] attacks The most target in Mexico is Bus with [15] attacks The most target in Moldova is Bridge Over Dnestr River with [2] attacks The most target in Montenegro is Editor-in-Chief: Mihail Jovic with [1] attacks The most target in Morocco is student member with [3] attacks The most target in Mozambique is Bus with [11] attacks The most target in Myanmar is Police Post with [25] attacks The most target in Namibia is Vehicle with [4] attacks The most target in Nepal is Office with [23] attacks The most target in Netherlands is Mosque with [5] attacks The most target in New Caledonia is Gendarme with [2] attacks The most target in New Hebrides is radio station with [1] attacks The most target in New Zealand is A scrap yard with [1] attacks The most target in Nicaragua is Military Unit with [219] attacks The most target in Niger is Village with [17] attacks The most target in Nigeria is Village with [653] attacks The most target in North Korea is Kim Chongnil Supporters with [1] attacks The most target in North Yemen is 'Add al-Fattah Isma'il, secretery general with [1] attacks The most target in Norway is Turkish Embassy with [3] attacks The most target in Pakistan is Vehicle with [444] attacks The most target in Panama is ranches with [6] attacks The most target in Papua New Guinea is Unit with [3] attacks The most target in Paraguay is Bus with [11] attacks The most target in People's Republic of the Congo is Ambassador: Nahum Gershom with [1] attacks The most target in Peru is High tension line tower with [116] attacks The most target in Philippines is Soldiers with [138] attacks The most target in Poland is Artur G. with [1] attacks The most target in Portugal is Privately owned vehicle of FRG Military Personnel stationed at NATO Base, * with [8] attacks The most target in Qatar is Civilians in Doha with [1] attacks The most target in Republic of the Congo is Police Station with [7] attacks The most target in Rhodesia is train with [2] attacks The most target in Romania is A gas station in Bucharest with [1] attacks The most target in Russia is Officers with [54] attacks The most target in Rwanda is Civilians with [11] attacks The most target in Saudi Arabia is Patrol with [25] attacks The most target in Senegal is Truck with [2] attacks The most target in Serbia is A police vehicle was targeted in the attack. with [1] attacks The most target in Serbia-Montenegro is A police checkpoint in Veliki Trnovac with [1] attacks The most target in Seychelles is Air India Boeing 707 (on way from Zimbabwe to Bombay) with [1] attacks The most target in Sierra Leone is Unit with [3] attacks The most target in Singapore is Building with [1] attacks The most target in Slovak Republic is A pharmacy in Bratislava with [1] attacks The most target in Slovenia is Defense Ministry with [1] attacks The most target in Solomon Islands is Airplane belonging to Solomon Airlines with [1] attacks The most target in Somalia is Soldiers with [173] attacks The most target in South Africa is SAP personnel with [37] attacks The most target in South Korea is Ambassador to South Korea: Mark Lippert with [1] attacks The most target in South Sudan is Soldiers with [13] attacks The most target in South Vietnam is CV-880 with [1] attacks The most target in South Yemen is Israeli Oil Tanker Coral Sea with [1] attacks The most target in Soviet Union is Customs Post with [5] attacks The most target in Spain is Bank with [57] attacks The most target in Sri Lanka is Unit with [77] attacks The most target in St. Kitts and Nevis is Vehicle with [1] attacks The most target in St. Lucia is Religious representatives and worshippers at the Basilica of the Immaculate Conception in Castries with [1] attacks The most target in Sudan is Village with [62] attacks The most target in Suriname is station with [3] attacks The most target in Swaziland is 8 vehicles with [1] attacks The most target in Sweden is Refugee Center with [8] attacks The most target in Switzerland is Diplomat with [5] attacks The most target in Syria is Neighborhood with [218] attacks The most target in Taiwan is Pavement, major street with [11] attacks The most target in Tajikistan is Russian Military with [10] attacks The most target in Tanzania is Church with [10] attacks The most target in Thailand is Patrol with [145] attacks The most target in Togo is Home of Party official with [2] attacks The most target in Trinidad and Tobago is Bus with [3] attacks The most target in Tunisia is Patrol with [9] attacks The most target in Turkey is Vehicle with [155] attacks The most target in Turkmenistan is Saparmurat Niyazov, the President of Turkmenistan with [1] attacks The most target in Uganda is Police Station with [7] attacks The most target in Ukraine is Soldiers with [297] attacks The most target in United Arab Emirates is Airliner with [1] attacks The most target in United Kingdom is House with [61] attacks The most target in United States is Church with [34] attacks The most target in Uruguay is Acodike Supergas with [1] attacks The most target in Uzbekistan is Government building in Tashkent with [2] attacks The most target in Vanuatu is Advisor to VMF with [1] attacks The most target in Vatican City is Pope, John Paul II (60), Head with [1] attacks The most target in Venezuela is Bus with [8] attacks The most target in Vietnam is Airport with [1] attacks The most target in Wallis and Futuna is Territorial Assembly Bldg. with [1] attacks The most target in West Bank and Gaza Strip is Soldiers with [80] attacks The most target in West Germany (FRG) is bank with [7] attacks The most target in Western Sahara is Patrol (all Italians) with [1] attacks The most target in Yemen is Checkpoint with [108] attacks The most target in Yugoslavia is Police Staion with [3] attacks The most target in Zaire is Embassy Official with [3] attacks The most target in Zambia is A village in Chavuma District with [8] attacks The most target in Zimbabwe is 3 buses a moving van, some cars with [1] attacks
Q4: What is the most attack_type in each country?
for x in df.groupby(df.country).sum().sort_values(by="country").index:
print("The most attack_type in",x,"is",df[df.country == x].groupby("attack_type").count().sort_values('nkill',ascending = False)[:1].index[0],"with",df[df.country == x].groupby("target").count().sort_values('nkill',ascending = False)[:1].day.values,"attacks")
The most attack_type in Afghanistan is Bombing/Explosion with [659] attacks The most attack_type in Albania is Bombing/Explosion with [3] attacks The most attack_type in Algeria is Bombing/Explosion with [55] attacks The most attack_type in Angola is Bombing/Explosion with [113] attacks The most attack_type in Argentina is Bombing/Explosion with [19] attacks The most attack_type in Armenia is Bombing/Explosion with [1] attacks The most attack_type in Australia is Facility/Infrastructure Attack with [8] attacks The most attack_type in Austria is Bombing/Explosion with [5] attacks The most attack_type in Azerbaijan is Bombing/Explosion with [1] attacks The most attack_type in Bahamas is Bombing/Explosion with [1] attacks The most attack_type in Bahrain is Bombing/Explosion with [28] attacks The most attack_type in Bangladesh is Bombing/Explosion with [60] attacks The most attack_type in Barbados is Bombing/Explosion with [1] attacks The most attack_type in Belarus is Bombing/Explosion with [1] attacks The most attack_type in Belgium is Bombing/Explosion with [3] attacks The most attack_type in Belize is Assassination with [1] attacks The most attack_type in Benin is Bombing/Explosion with [1] attacks The most attack_type in Bhutan is Bombing/Explosion with [1] attacks The most attack_type in Bolivia is Bombing/Explosion with [12] attacks The most attack_type in Bosnia-Herzegovina is Bombing/Explosion with [5] attacks The most attack_type in Botswana is Bombing/Explosion with [1] attacks The most attack_type in Brazil is Assassination with [9] attacks The most attack_type in Brunei is Assassination with [5] attacks The most attack_type in Bulgaria is Bombing/Explosion with [2] attacks The most attack_type in Burkina Faso is Armed Assault with [5] attacks The most attack_type in Burundi is Armed Assault with [24] attacks The most attack_type in Cambodia is Bombing/Explosion with [5] attacks The most attack_type in Cameroon is Bombing/Explosion with [33] attacks The most attack_type in Canada is Bombing/Explosion with [4] attacks The most attack_type in Central African Republic is Armed Assault with [25] attacks The most attack_type in Chad is Armed Assault with [6] attacks The most attack_type in Chile is Bombing/Explosion with [55] attacks The most attack_type in China is Bombing/Explosion with [11] attacks The most attack_type in Colombia is Bombing/Explosion with [140] attacks The most attack_type in Comoros is Facility/Infrastructure Attack with [2] attacks The most attack_type in Costa Rica is Bombing/Explosion with [2] attacks The most attack_type in Croatia is Bombing/Explosion with [3] attacks The most attack_type in Cuba is Bombing/Explosion with [6] attacks The most attack_type in Cyprus is Bombing/Explosion with [4] attacks The most attack_type in Czech Republic is Bombing/Explosion with [2] attacks The most attack_type in Czechoslovakia is Bombing/Explosion with [1] attacks The most attack_type in Democratic Republic of the Congo is Armed Assault with [82] attacks The most attack_type in Denmark is Bombing/Explosion with [1] attacks The most attack_type in Djibouti is Bombing/Explosion with [1] attacks The most attack_type in Dominica is Armed Assault with [1] attacks The most attack_type in Dominican Republic is Bombing/Explosion with [13] attacks The most attack_type in East Germany (GDR) is Facility/Infrastructure Attack with [3] attacks The most attack_type in East Timor is Bombing/Explosion with [1] attacks The most attack_type in Ecuador is Bombing/Explosion with [4] attacks The most attack_type in Egypt is Bombing/Explosion with [217] attacks The most attack_type in El Salvador is Armed Assault with [220] attacks The most attack_type in Equatorial Guinea is Armed Assault with [1] attacks The most attack_type in Eritrea is Bombing/Explosion with [1] attacks The most attack_type in Estonia is Bombing/Explosion with [1] attacks The most attack_type in Ethiopia is Bombing/Explosion with [10] attacks The most attack_type in Fiji is Facility/Infrastructure Attack with [1] attacks The most attack_type in Finland is Facility/Infrastructure Attack with [4] attacks The most attack_type in France is Bombing/Explosion with [50] attacks The most attack_type in French Guiana is Facility/Infrastructure Attack with [1] attacks The most attack_type in French Polynesia is Facility/Infrastructure Attack with [1] attacks The most attack_type in Gabon is Assassination with [1] attacks The most attack_type in Gambia is Assassination with [1] attacks The most attack_type in Georgia is Bombing/Explosion with [5] attacks The most attack_type in Germany is Facility/Infrastructure Attack with [22] attacks The most attack_type in Ghana is Bombing/Explosion with [2] attacks The most attack_type in Greece is Bombing/Explosion with [32] attacks The most attack_type in Grenada is Assassination with [1] attacks The most attack_type in Guadeloupe is Bombing/Explosion with [1] attacks The most attack_type in Guatemala is Assassination with [109] attacks The most attack_type in Guinea is Assassination with [2] attacks The most attack_type in Guinea-Bissau is Armed Assault with [1] attacks The most attack_type in Guyana is Bombing/Explosion with [2] attacks The most attack_type in Haiti is Assassination with [2] attacks The most attack_type in Honduras is Bombing/Explosion with [9] attacks The most attack_type in Hong Kong is Bombing/Explosion with [1] attacks The most attack_type in Hungary is Bombing/Explosion with [4] attacks The most attack_type in Iceland is Bombing/Explosion with [1] attacks The most attack_type in India is Bombing/Explosion with [253] attacks The most attack_type in Indonesia is Bombing/Explosion with [25] attacks The most attack_type in International is Bombing/Explosion with [1] attacks The most attack_type in Iran is Bombing/Explosion with [8] attacks The most attack_type in Iraq is Bombing/Explosion with [2906] attacks The most attack_type in Ireland is Bombing/Explosion with [28] attacks The most attack_type in Israel is Bombing/Explosion with [82] attacks The most attack_type in Italy is Bombing/Explosion with [31] attacks The most attack_type in Ivory Coast is Armed Assault with [6] attacks The most attack_type in Jamaica is Armed Assault with [2] attacks The most attack_type in Japan is Facility/Infrastructure Attack with [9] attacks The most attack_type in Jordan is Bombing/Explosion with [4] attacks The most attack_type in Kazakhstan is Bombing/Explosion with [2] attacks The most attack_type in Kenya is Bombing/Explosion with [34] attacks The most attack_type in Kosovo is Bombing/Explosion with [4] attacks The most attack_type in Kuwait is Bombing/Explosion with [2] attacks The most attack_type in Kyrgyzstan is Bombing/Explosion with [3] attacks The most attack_type in Laos is Bombing/Explosion with [2] attacks The most attack_type in Latvia is Bombing/Explosion with [1] attacks The most attack_type in Lebanon is Bombing/Explosion with [50] attacks The most attack_type in Lesotho is Bombing/Explosion with [1] attacks The most attack_type in Liberia is Armed Assault with [1] attacks The most attack_type in Libya is Bombing/Explosion with [65] attacks The most attack_type in Lithuania is Bombing/Explosion with [1] attacks The most attack_type in Luxembourg is Bombing/Explosion with [3] attacks The most attack_type in Macau is Bombing/Explosion with [4] attacks The most attack_type in Macedonia is Bombing/Explosion with [3] attacks The most attack_type in Madagascar is Bombing/Explosion with [1] attacks The most attack_type in Malawi is Armed Assault with [1] attacks The most attack_type in Malaysia is Bombing/Explosion with [5] attacks The most attack_type in Maldives is Facility/Infrastructure Attack with [3] attacks The most attack_type in Mali is Bombing/Explosion with [41] attacks The most attack_type in Malta is Bombing/Explosion with [3] attacks The most attack_type in Martinique is Bombing/Explosion with [1] attacks The most attack_type in Mauritania is Armed Assault with [1] attacks The most attack_type in Mauritius is Assassination with [1] attacks The most attack_type in Mexico is Armed Assault with [15] attacks The most attack_type in Moldova is Bombing/Explosion with [2] attacks The most attack_type in Montenegro is Bombing/Explosion with [1] attacks The most attack_type in Morocco is Bombing/Explosion with [3] attacks The most attack_type in Mozambique is Armed Assault with [11] attacks The most attack_type in Myanmar is Bombing/Explosion with [25] attacks The most attack_type in Namibia is Bombing/Explosion with [4] attacks The most attack_type in Nepal is Bombing/Explosion with [23] attacks The most attack_type in Netherlands is Bombing/Explosion with [5] attacks The most attack_type in New Caledonia is Bombing/Explosion with [2] attacks The most attack_type in New Hebrides is Bombing/Explosion with [1] attacks The most attack_type in New Zealand is Unarmed Assault with [1] attacks The most attack_type in Nicaragua is Armed Assault with [219] attacks The most attack_type in Niger is Armed Assault with [17] attacks The most attack_type in Nigeria is Armed Assault with [653] attacks The most attack_type in North Korea is Armed Assault with [1] attacks The most attack_type in North Yemen is Assassination with [1] attacks The most attack_type in Norway is Facility/Infrastructure Attack with [3] attacks The most attack_type in Pakistan is Bombing/Explosion with [444] attacks The most attack_type in Panama is Bombing/Explosion with [6] attacks The most attack_type in Papua New Guinea is Armed Assault with [3] attacks The most attack_type in Paraguay is Armed Assault with [11] attacks The most attack_type in People's Republic of the Congo is Assassination with [1] attacks The most attack_type in Peru is Bombing/Explosion with [116] attacks The most attack_type in Philippines is Armed Assault with [138] attacks The most attack_type in Poland is Bombing/Explosion with [1] attacks The most attack_type in Portugal is Bombing/Explosion with [8] attacks The most attack_type in Qatar is Bombing/Explosion with [1] attacks The most attack_type in Republic of the Congo is Bombing/Explosion with [7] attacks The most attack_type in Rhodesia is Bombing/Explosion with [2] attacks The most attack_type in Romania is Assassination with [1] attacks The most attack_type in Russia is Bombing/Explosion with [54] attacks The most attack_type in Rwanda is Armed Assault with [11] attacks The most attack_type in Saudi Arabia is Bombing/Explosion with [25] attacks The most attack_type in Senegal is Armed Assault with [2] attacks The most attack_type in Serbia is Bombing/Explosion with [1] attacks The most attack_type in Serbia-Montenegro is Bombing/Explosion with [1] attacks The most attack_type in Seychelles is Bombing/Explosion with [1] attacks The most attack_type in Sierra Leone is Armed Assault with [3] attacks The most attack_type in Singapore is Bombing/Explosion with [1] attacks The most attack_type in Slovak Republic is Bombing/Explosion with [1] attacks The most attack_type in Slovenia is Bombing/Explosion with [1] attacks The most attack_type in Solomon Islands is Assassination with [1] attacks The most attack_type in Somalia is Bombing/Explosion with [173] attacks The most attack_type in South Africa is Bombing/Explosion with [37] attacks The most attack_type in South Korea is Facility/Infrastructure Attack with [1] attacks The most attack_type in South Sudan is Armed Assault with [13] attacks The most attack_type in South Vietnam is Bombing/Explosion with [1] attacks The most attack_type in South Yemen is Bombing/Explosion with [1] attacks The most attack_type in Soviet Union is Bombing/Explosion with [5] attacks The most attack_type in Spain is Bombing/Explosion with [57] attacks The most attack_type in Sri Lanka is Bombing/Explosion with [77] attacks The most attack_type in St. Kitts and Nevis is Unarmed Assault with [1] attacks The most attack_type in St. Lucia is Armed Assault with [1] attacks The most attack_type in Sudan is Armed Assault with [62] attacks The most attack_type in Suriname is Armed Assault with [3] attacks The most attack_type in Swaziland is Bombing/Explosion with [1] attacks The most attack_type in Sweden is Facility/Infrastructure Attack with [8] attacks The most attack_type in Switzerland is Bombing/Explosion with [5] attacks The most attack_type in Syria is Bombing/Explosion with [218] attacks The most attack_type in Taiwan is Facility/Infrastructure Attack with [11] attacks The most attack_type in Tajikistan is Bombing/Explosion with [10] attacks The most attack_type in Tanzania is Bombing/Explosion with [10] attacks The most attack_type in Thailand is Bombing/Explosion with [145] attacks The most attack_type in Togo is Bombing/Explosion with [2] attacks The most attack_type in Trinidad and Tobago is Assassination with [3] attacks The most attack_type in Tunisia is Armed Assault with [9] attacks The most attack_type in Turkey is Bombing/Explosion with [155] attacks The most attack_type in Turkmenistan is Assassination with [1] attacks The most attack_type in Uganda is Armed Assault with [7] attacks The most attack_type in Ukraine is Bombing/Explosion with [297] attacks The most attack_type in United Arab Emirates is Bombing/Explosion with [1] attacks The most attack_type in United Kingdom is Bombing/Explosion with [61] attacks The most attack_type in United States is Bombing/Explosion with [34] attacks The most attack_type in Uruguay is Bombing/Explosion with [1] attacks The most attack_type in Uzbekistan is Bombing/Explosion with [2] attacks The most attack_type in Vanuatu is Hostage Taking (Barricade Incident) with [1] attacks The most attack_type in Vatican City is Assassination with [1] attacks The most attack_type in Venezuela is Bombing/Explosion with [8] attacks The most attack_type in Vietnam is Facility/Infrastructure Attack with [1] attacks The most attack_type in Wallis and Futuna is Facility/Infrastructure Attack with [1] attacks The most attack_type in West Bank and Gaza Strip is Armed Assault with [80] attacks The most attack_type in West Germany (FRG) is Bombing/Explosion with [7] attacks The most attack_type in Western Sahara is Bombing/Explosion with [1] attacks The most attack_type in Yemen is Bombing/Explosion with [108] attacks The most attack_type in Yugoslavia is Armed Assault with [3] attacks The most attack_type in Zaire is Bombing/Explosion with [3] attacks The most attack_type in Zambia is Bombing/Explosion with [8] attacks The most attack_type in Zimbabwe is Bombing/Explosion with [1] attacks
Q5: what is the most Region has kills?
pltbar(df.groupby(df.region).sum().sort_values(by="nkill", ascending=False)[:5].nkill.index, df.groupby(df.region).sum().sort_values(by="nkill", ascending=False)[:5].nkill.values, "Region", "#of kills", "Top 5 Regions has kills")
plt.xticks(rotation = 45);
df.groupby(df.region).sum().sort_values(by="nkill", ascending=False)[:5].nkill
region Middle East & North Africa 115913 South Asia 85891 Sub-Saharan Africa 63695 South America 22832 Central America & Caribbean 21118 Name: nkill, dtype: int32
Q6: What is the top 5 Regions in average kills by attack?
pltbar(df.groupby(df.region).mean().sort_values("nkill", ascending=False)[:5].index, df.groupby(df.region).mean().sort_values("nkill", ascending=False).nkill[:5].values, "Region", "Average kills by attack", "Top 5 Regions in average kills by attack")
plt.xticks(rotation = 45);
df.groupby(df.region).mean().sort_values("nkill", ascending=False)[:5].nkill
region Sub-Saharan Africa 4.706295 Central America & Caribbean 3.318875 Middle East & North Africa 2.806882 South Asia 2.277249 East Asia 1.752000 Name: nkill, dtype: float64
Q7: What is the most target in each Region?
for x in df.groupby(df.region).sum().sort_values(by="region").index:
print("The most target in",x,"is",df[df.region == x].groupby("target").count().sort_values('nkill',ascending = False)[:1].index[0],"with",df[df.region == x].groupby("target").count().sort_values('nkill',ascending = False)[:1].day.values,"attacks")
The most target in Australasia & Oceania is Church with [8] attacks The most target in Central America & Caribbean is Military Unit with [557] attacks The most target in Central Asia is Russian Military with [12] attacks The most target in East Asia is Pavement, major street with [11] attacks The most target in Eastern Europe is Soldiers with [304] attacks The most target in Middle East & North Africa is Civilians with [3428] attacks The most target in North America is Church with [37] attacks The most target in South America is Bus with [303] attacks The most target in South Asia is Civilians with [1255] attacks The most target in Southeast Asia is Patrol with [255] attacks The most target in Sub-Saharan Africa is Village with [911] attacks The most target in Western Europe is Bank with [142] attacks
Q8: What is the most attack_type in each Region?
for x in df.groupby(df.region).sum().sort_values(by="region").index:
print("The most attack_type in",x,"is",df[df.region == x].groupby("attack_type").count().sort_values('nkill',ascending = False)[:1].index[0],"with",df[df.region == x].groupby("target").count().sort_values('nkill',ascending = False)[:1].day.values,"attacks")
The most attack_type in Australasia & Oceania is Facility/Infrastructure Attack with [8] attacks The most attack_type in Central America & Caribbean is Armed Assault with [557] attacks The most attack_type in Central Asia is Bombing/Explosion with [12] attacks The most attack_type in East Asia is Bombing/Explosion with [11] attacks The most attack_type in Eastern Europe is Bombing/Explosion with [304] attacks The most attack_type in Middle East & North Africa is Bombing/Explosion with [3428] attacks The most attack_type in North America is Bombing/Explosion with [37] attacks The most attack_type in South America is Bombing/Explosion with [303] attacks The most attack_type in South Asia is Bombing/Explosion with [1255] attacks The most attack_type in Southeast Asia is Bombing/Explosion with [255] attacks The most attack_type in Sub-Saharan Africa is Armed Assault with [911] attacks The most attack_type in Western Europe is Bombing/Explosion with [142] attacks
df[df.country == "Iraq"].groupby("state").sum().nkill.sort_values(ascending = False)[:5]
state Baghdad 19405 Nineveh 13358 Al Anbar 9837 Saladin 9036 Diyala 6971 Name: nkill, dtype: int32
df[df.country == "Iraq"].groupby("city").sum().nkill.sort_values(ascending = False)[:5]
city Baghdad 19343 Mosul 6297 Tikrit 2526 Ramadi 2026 Baqubah 1680 Name: nkill, dtype: int32
Q10: What is the most target & attack_type in Middle East countries?
for x in df[df.region == "Middle East & North Africa"].groupby(df.country).sum().sort_values(by="nkill", ascending = False).index:
print("The most target in",x,"is",df[df.country == x].groupby("target").count().sort_values('nkill',ascending = False)[:1].index[0],"with",df[df.country == x].groupby("target").count().sort_values('nkill',ascending = False)[:1].nkill.values,"attacks")
The most target in Iraq is Civilians with [2906] attacks The most target in Syria is Neighborhood with [218] attacks The most target in Algeria is Unit with [55] attacks The most target in Yemen is Checkpoint with [108] attacks The most target in Turkey is Vehicle with [155] attacks The most target in Lebanon is Patrol with [50] attacks The most target in Egypt is Checkpoint with [217] attacks The most target in Libya is Soldiers with [65] attacks The most target in Israel is Civilians with [82] attacks The most target in Iran is Vehicle with [8] attacks The most target in West Bank and Gaza Strip is Soldiers with [80] attacks The most target in Saudi Arabia is Patrol with [25] attacks The most target in Tunisia is Patrol with [9] attacks The most target in Jordan is Offical with [4] attacks The most target in United Arab Emirates is Airliner with [1] attacks The most target in Morocco is student member with [3] attacks The most target in Kuwait is Unit with [2] attacks The most target in Bahrain is Officers with [28] attacks The most target in Qatar is Civilians in Doha with [1] attacks The most target in International is Tanker ship Limberg with [1] attacks The most target in North Yemen is 'Add al-Fattah Isma'il, secretery general with [1] attacks The most target in South Yemen is Israeli Oil Tanker Coral Sea with [1] attacks The most target in Western Sahara is Patrol (all Italians) with [1] attacks
for x in df[df.region == "Middle East & North Africa"].groupby(df.country).count().sort_values(by="day", ascending = False).index:
print("The most attack_type in",x,"is",df[df.country == x].groupby("attack_type").count().sort_values('nkill',ascending = False)[:1].index[0],"with",df[df.country == x].groupby("attack_type").count().sort_values('nkill',ascending = False)[:1].day.values,"attacks")
The most attack_type in Iraq is Bombing/Explosion with [16187] attacks The most attack_type in Turkey is Bombing/Explosion with [1647] attacks The most attack_type in Yemen is Bombing/Explosion with [1098] attacks The most attack_type in Algeria is Bombing/Explosion with [958] attacks The most attack_type in Lebanon is Bombing/Explosion with [1214] attacks The most attack_type in Egypt is Bombing/Explosion with [881] attacks The most attack_type in West Bank and Gaza Strip is Armed Assault with [802] attacks The most attack_type in Libya is Bombing/Explosion with [831] attacks The most attack_type in Israel is Bombing/Explosion with [1164] attacks The most attack_type in Syria is Bombing/Explosion with [1289] attacks The most attack_type in Iran is Bombing/Explosion with [260] attacks The most attack_type in Saudi Arabia is Bombing/Explosion with [167] attacks The most attack_type in Bahrain is Bombing/Explosion with [102] attacks The most attack_type in Jordan is Bombing/Explosion with [34] attacks The most attack_type in Tunisia is Armed Assault with [32] attacks The most attack_type in Kuwait is Bombing/Explosion with [25] attacks The most attack_type in Morocco is Bombing/Explosion with [14] attacks The most attack_type in United Arab Emirates is Bombing/Explosion with [8] attacks The most attack_type in Qatar is Bombing/Explosion with [2] attacks The most attack_type in North Yemen is Assassination with [2] attacks The most attack_type in Western Sahara is Bombing/Explosion with [2] attacks The most attack_type in South Yemen is Bombing/Explosion with [1] attacks The most attack_type in International is Bombing/Explosion with [1] attacks
Q11: What is the most target & attack_type in Iraq states?
for x in df[df.country == "Iraq"].groupby(df.state).count().sort_values(by="day", ascending = False).index:
print("The most target in",x,"is",df[df.state == x].groupby("target").count().sort_values('nkill',ascending = False)[:1].index[0],"with",df[df.state == x].groupby("target").count().sort_values('nkill',ascending = False)[:1].day.values,"attacks")
The most target in Baghdad is Civilians with [1411] attacks The most target in Saladin is Patrol with [266] attacks The most target in Diyala is Civilians with [409] attacks The most target in Nineveh is Civilians with [223] attacks The most target in Al Anbar is Civilians with [231] attacks The most target in Kirkuk is Civilians with [134] attacks The most target in Babil is Civilians with [165] attacks The most target in Basra is Civilians with [9] attacks The most target in Karbala is Civilians with [17] attacks The most target in Wasit is Civilians with [15] attacks The most target in At Tamim is Police patrol in Kirkuk, Iraq with [3] attacks The most target in Arbil is Officer with [8] attacks The most target in Dhi Qar is Civilians with [8] attacks The most target in Sulaymaniyah is Members with [4] attacks The most target in Najaf is Civilians with [4] attacks The most target in Al Qadisiyah is Checkpoint with [5] attacks The most target in Maysan is Civilians with [4] attacks The most target in Qadisiyah is Members with [2] attacks The most target in Dihok is Vehicle with [3] attacks The most target in Muthanna is Checkpoint with [2] attacks The most target in NIneveh is Camp with [1] attacks The most target in Khost is Vehicle with [25] attacks
for x in df[df.country == "Iraq"].groupby(df.state).count().sort_values(by="day", ascending = False).index:
print("The most attack_type in",x,"is",df[df.state == x].groupby("attack_type").count().sort_values('nkill',ascending = False)[:1].index[0],"with",df[df.state == x].groupby("attack_type").count().sort_values('nkill',ascending = False)[:1].day.values,"attacks")
The most attack_type in Baghdad is Bombing/Explosion with [5962] attacks The most attack_type in Saladin is Bombing/Explosion with [2135] attacks The most attack_type in Diyala is Bombing/Explosion with [2030] attacks The most attack_type in Nineveh is Bombing/Explosion with [1653] attacks The most attack_type in Al Anbar is Bombing/Explosion with [1988] attacks The most attack_type in Kirkuk is Bombing/Explosion with [938] attacks The most attack_type in Babil is Bombing/Explosion with [882] attacks The most attack_type in Basra is Bombing/Explosion with [139] attacks The most attack_type in Karbala is Bombing/Explosion with [100] attacks The most attack_type in Wasit is Bombing/Explosion with [74] attacks The most attack_type in At Tamim is Bombing/Explosion with [54] attacks The most attack_type in Arbil is Bombing/Explosion with [61] attacks The most attack_type in Dhi Qar is Bombing/Explosion with [43] attacks The most attack_type in Sulaymaniyah is Bombing/Explosion with [26] attacks The most attack_type in Najaf is Bombing/Explosion with [30] attacks The most attack_type in Al Qadisiyah is Bombing/Explosion with [20] attacks The most attack_type in Maysan is Bombing/Explosion with [16] attacks The most attack_type in Qadisiyah is Armed Assault with [11] attacks The most attack_type in Dihok is Bombing/Explosion with [12] attacks The most attack_type in Muthanna is Bombing/Explosion with [14] attacks The most attack_type in NIneveh is Hostage Taking (Kidnapping) with [1] attacks The most attack_type in Khost is Bombing/Explosion with [277] attacks
df.to_csv("Modified")
- The most Region has terrorism attacks and killed people in the world is Middle East & North Africa with $41296$ attacks and $115913$ peoples killed by terrorists.
- The most Country has terrorism attacks and killed people in the Middle East and the World is Iraq with $21066$ attacks and $68408$ peoples killed by terrorists.
- The most Governate has terrorism attacks in Iraq and the World is Baghdad with $7129$ attacks.
- The most Weapons used in the attacks is Bombing/Explosion with $74250$ attacks using this weapon.
- The most Targets by Terrorists is Civilians with $5688$ attacks on it.
- The most country has #of kills per attack is Barbados with an average $25$ kills per attack.
- The most Region has #of kills per attack is Sub-Saharan Africa with an average $4.7$ kills per attack.
Recommendation:
Finally, Iraq faced a fatal war caused by the USA, which caused immense suffering and had far-reaching consequences for the lives of its people. The country's infrastructure has been severely damaged. Despite these challenges, the people of Iraq have shown remarkable resilience, and determination in the face of adversity and terrorism. Ultimately, it is only through cooperation and dialogue that a lasting solution can be found to end the suffering caused by this devastating war.Data Limitation¶
As I said before the dataset is not considering the World Wars as terrorist acts and did not waste the lives of millions of people in the other hand it considers the war in Iraq as a terrorist act, which is very confusing for me actually.